The book shares real-world experiences and knowledge about advanced application of Machine learning, Artificial intelligence and IOT in healthcare. The book describes issues, trends scenarios for various domains and industry sectors.

This book focuses on Artificial Intelligence and Machine Learning technologies and how they are progressively being incorporated into a wide range of products, including consumer gadgets, "smart" personal assistants, cutting-edge medical diagnostic systems, and quantum computing systems. This concise reference book offers a broad overview of the most important trends and discusses how these trends and technologies are being created and employed in the applications they are finding use in. Artificial Intelligence and Machine Learning: An Intelligent Perspective of Emerging Technologies offers a broad package involving the incubation of ML and AI with various emerging technologies such as IoT, Healthcare, Smart Cities, Robotics, and more. The book discusses various data collection and data transformation techniques and maps the legal and ethical issues of data-driven e-healthcare systems while covering the possible ways to resolve them. The book explores different techniques on how AI can be used to create better virtual reality experiences and deals with the techniques and possible ways to merge the power of AI and IoT to create smart home appliances. With contributions from experts in the field, this reference book is useful to healthcare professionals, researchers, and students of industrial engineering, systems engineering, biomedical, computer science, electronics, and communications engineering.

This book provides use case scenarios of machine learning, artificial intelligence, and real-time domains to supplement cyber security operations and proactively predict attacks and preempt cyber incidents. The authors discuss cybersecurity incident planning, starting from a draft response plan, to assigning responsibilities, to use of external experts, to equipping organization teams to address incidents, to preparing communication strategy and cyber insurance. They also discuss classifications and methods to detect cybersecurity incidents, how to organize the incident response team, how to conduct situational awareness, how to contain and eradicate incidents, and how to cleanup and recover. The book shares real-world experiences and knowledge from authors from academia and industry.

This book provides use case scenarios of machine learning, artificial intelligence, and real-time domains to supplement cyber security operations and proactively predict attacks and preempt cyber incidents. The authors discuss cybersecurity incident planning, starting from a draft response plan, to assigning responsibilities, to use of external experts, to equipping organization teams to address incidents, to preparing communication strategy and cyber insurance. They also discuss classifications and methods to detect cybersecurity incidents, how to organize the incident response team, how to conduct situational awareness, how to contain and eradicate incidents, and how to cleanup and recover. The book shares real-world experiences and knowledge from authors from academia and industry.